Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The spoken message in a scientific talk is enhanced by well-prepared slides that are simple, clear, legible, and pleasing to the eye. Good slides can create visual images that endure in the audience's mind long after the speaker has finished. Poorly prepared slides, however, detract from both the speaker and the intended message. Poor slides have features that hinder communication, such as small letters, too much text, dark images on dark backgrounds, outlandish colors, complex figures, or large tables. Poor slides create lasting images, too, but of an undesirable kind. In an effort to encourage scientists to reconsider the effectiveness of their slides, we provide some guidelines for slide preparation. We hope that our opinions will stimulate speakers to prepare slides that enhance, rather than detract from, the spoken words (see Smith 1957, Toft 1998). First, we present our top 10 recommendations, in decreasing order of importance. Then, we offer six additional ideas that also should aid in preparing effective slides and talks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it